Creating A Smarter World

Ground Your Workforce AI Strategy In Human Experience:

J. P. Gownder Vice President, Principal Analyst, Forrester With contributors: James McQuivey, PhD, Shynise McElveen, and Bill Nagel

Too many leaders see their workforce AI deployment as primarily a technology and data exercise. That perception couldn’t be further from the truth: Today’s AI remains intimately tied to human users, whose experience with the technology will be a principal determinant of its success or failure. Framing AI as a tool that builds new opportunities for employees in their jobs, lives, and future careers can help. And designing a human-centric culture of AI to reinforce positive practices, beliefs, and behaviors will drive both employee experience and business success.

Underinvesting In Humans Will Undercut Your AI Strategy Faced with a continuous flow of breaking news about generative AI (genAI), it’s tempting to spend your time keeping up with the latest technology developments. Consider DeepSeek: In less than a week, the Chinese model disrupted our understanding of the costs and computing resources required for effective AI. OpenAI’s Operator and other AI agents are moving the genAI conversation from words to actions, promising to take on new levels of human labor autonomously. In the longer term, AI will act as an interface that simplifies human interactions with technology and magnifies our capabilities.

But the journey to making AI useful to organizations runs through the human workforce. To use genAI and other forms of AI productively, employees need a sufficient artificial intelligence quotient (AIQ), which encompasses understanding, skills, and ethical awareness. Gaps in employee readiness create significant barriers to success with frequently considered solutions like Microsoft 365 Copilot. Putting humans at the center of your AI strategy is crucial because:

Most AI augments humans rather than replacing them. Despite the continued growth of autonomous agentic AI, humans remain central to how work gets done — and are vital to AI deployments themselves. As West Monroe’s Erik Brown told us, “Any kind of AI deployment requires humans in the loop. For genAI, you must make sure employees can easily access tools and learn how to use them effectively. But even in more automated use cases, there’s always a human being overseeing the process, ensuring quality, and providing human judgment.” AI decision-makers see augmentation as important, too: In Forrester’s State Of AI Survey, 2024, 38% said that the increase in AI use for personal productivity was one of the ways that AI has had a positive impact on their organization in the past year — the most frequent response.

Employee buy-in is crucial to successful adoption. Employee rejection of AI tools renders your investment worthless. Clients regularly tell us that 30% to 60% of employees with licenses don’t use Microsoft 365 Copilot often enough, so they transfer the license to other employees, hoping they will use it. But throwing spaghetti against the wall hoping it will stick has its limits; sometimes you’ve got to prove the value of AI. One firm’s dispatch center employees resisted adopting AI software that could help them assign field service agents more effectively. So the company held a “hands-free Tuesday” in which the dispatchers had to trust the AI without any intervention. The demo impressed dispatchers and increased their acceptance.

Misuse can lead to quality breakdowns and ethical lapses. When employees lack skill with AI, it generates serious business risks. “Without the right information and education, employees may overrely on the output of genAI. The human role in quality assurance is critical, because the systems aren’t always right,” Foundever VP of learning and development Matthew Stone told us. GenAI’s tendency to hallucinate or generate coherent nonsense remains a problem. While medical providers rushed to adopt transcription tools based on OpenAI’s Whisper model, it’s “prone to making up chunks of text or even entire sentences.”

Excessive Reliance On AI Undercuts Human Performance

Compounding these challenges, an unwarranted focus on AI at the expense of employees can actually weaken their effectiveness. As a leader at a financial services firm told us, “We really need to assure employees about the human experience of AI. If they enter fight or flight mode, they can’t hear anything we want them to learn about using AI, and it’s a huge distraction. We lose their attention, and morale dips.” Employees have questions and harbor misgivings about workforce AI (see Figure 1). Deploying AI without employing a human-centric strategy risks bad outcomes, because:

Cognitive offloading risks cognitive laziness and dependence. A recent academic study reveals the double-edged sword of workforce AI. On the one hand, using AI can help employees by “delegating tasks such as memory retention, decision-making, and information retrieval to external systems,” freeing up time and energy for other work. On the other hand, it may lead to a reduction in cognitive effort, fostering what “researchers refer to as ‘cognitive laziness.’” The study shows a negative correlation between frequent AI tool use and critical thinking, meaning that people who use AI tools more often see decreasing critical thinking skills. In some cases, they become dependent and forget how to conduct tasks themselves.

Too many leaders engage in "satisficing." Satisficing means accepting an adequate result rather than the optimal solution; plugging AI into workflows instead of humans (or humans plus AI) too often entails this tradeoff. As Coty Smith, director of AI and digital innovation at Genesys, told us: “AI can’t always understand context or deliver empathy — and if you accept that as the cheaper alternative, it’s a net loss to the business. Employees need to be both objective and subjective when interpreting what AI provides them: The AI might be factually correct, but is it the right response to give to the customer based on their actual need?” Getting a “good enough” answer from AI may not be good enough to help you achieve customer obsession.

Deploying without a human focus magnifies fears. A Gallup survey found that 75% of US workers believe that AI will reduce the total number of jobs over the next 10 years; 77% don’t trust businesses to use AI responsibly. One leader who deployed AI to help HR recruiters “got pushback because the recruiters felt it was a criticism of them not doing their jobs well.” But a human focus can help.

“There’s a lot of hype that AI might take someone’s job, but if you get out front and say ‘Here are the technologies we have, here’s what we are developing, and here’s how you, the employee, fit into that,’ you can alleviate those concerns.” (Aron Meyer, digital workplace solutions manager, Unisys)

Deployments lead employees to feel “AI overload.” An Upwork survey found that 77% of employees believe that AI tools have actually decreased their productivity. The learning curve and training, in which organizations underinvest, already take up a lot of time, in addition to the time employees spend editing and double-checking AI outputs. One in five respondents said that their bosses are already giving them more work after rolling out AI tools.

The figure displays many thoughts and concerns of employees related to the implementation of AI, including ‘Am I confident I can adapt to using AI tools at work?’, ‘Do I have the skills, understanding, and ethics to use AI?’, ‘Will AI steal my job?’, ‘Is AI worth the time I need to invest in learning it?’, ‘What’s in it for me?’, and ‘Am I overconfident about how well AI works?’

Employees Harbor Misgivings About Workforce AI

The rapid advance of genAI in the workplace — in 2024, 56% of business and technology professionals said that their firm has an active deployment of genAI of some sort — leaves employees wondering what their role in this new world is, and what’s in it for them. You’ll find that deploying workforce AI becomes easier when you frame the effort as an opportunity builder for employees. That is, introducing AI into their workflows gives them the opportunity to take boring, predictable tasks off their plates, improve their competitiveness in the job market, and solve hard problems. To frame AI as an opportunity builder, you can:

1. Showcase success stories. To overcome employees’ concerns about whether learning AI is worth their time, highlight successes and outcomes enjoyed by their peers. “Celebrating successes helps employees see that the work you put in to master these tools is genuinely worthwhile. It helps them overcome the cognitive bias we all have toward not squandering energy on activities that we believe don’t benefit us,” said Ruth Svensson, psychologist and global head of people and HR CoE at KPMG. Present success stories at weekly lunch-and-learns or via short (5 minutes or less) video interviews of early adopter employees — both of which are best practices for a comprehensive workforce genAI learning strategy.
2. Build skills and career paths. An HR leader at a global energy company confided that “I don’t see a lot of my peers rewriting job descriptions to take into account how AI is changing people’s jobs.” But some progress is being made: A business leader at an accounting and auditing firm told us, “We cultivate interest in AI by linking it to their career trajectory, which also requires a learning mindset. They start to feel that ‘It’s just a technology to move stuff off my plate and help me accelerate my career trajectory.’” Honor the acquisition of AI-related skills and the changing nature of work in employees’ job descriptions and salaries.
3. Solve intractable problems. Governments, financial institutions, and businesses still run billions of lines of code written in the 66-year-old programming language COBOL. But students no longer learn COBOL, and many developers for the language are retired. In the Netherlands, the Social Insurance Bank faced a talent crisis that required the company to entice COBOL developers out of retirement to rejoin the workforce. But AI can be used to bridge this gap: TuringBots like XMainframe trained on COBOL can help write code in the language, potentially obviating the need for human developers to write it themselves.

These are effective ways of framing the implementation of AI as an opportunity. The language outlined for the technology itself is ‘Allow me to take on rote tasks my human colleagues don’t want to do’ and ‘Allow me to help my human coworkers thrive and succeed.’ The employees framing includes ‘Remove boring, predictable tasks from my daily job,’ ‘Help me build new skills I can use in my career,’ ‘Allow me to showcase my successes to colleagues,’ ‘Offer me new career paths for the future,’ and ‘Help me solve previously intractable problems.’

Build A Human-Centric Culture To Support AI Success

Ultimately, you need a culture-based approach to AI readiness that is self-sustaining, human-centric, and employee-led. Forrester’s four characteristics of organizational culture model can help here.

For your workforce genAI strategy to succeed, it must be actively present across all four characteristics that determine and reinforce culture: shared purpose, behavioral norms, rituals, and artifacts. A culture-based approach to AI must reinforce the understanding, beliefs, practices, and actions that are most necessary to using AI to benefit both the organization and employees. And a culture-based approach AI depends on but will also help improve AIQ, a critical precondition for successful rollouts. Build your culture-based approach to human-centric AI within all four characteristics.

1. Shared purpose: common identity and sense of belonging. Build shared purpose by co-creating AI applications with those whose jobs will be reshaped by its use. As Ranjan Roy, SVP of strategy at e-commerce retailer Adore Me said, “When you are creating a process that involves content output with genAI, having the functional person directly involved in the creation of the prompt and process is crucial. If you do it without their participation, it becomes a competition between the employee and the genAI system.” Empowering employees to have direct roles as subject-matter experts and AI co-creators gives them a shared purpose with the people deploying AI rather than putting them into opposition.
2. Behavioral norms: engagement-driving resources. Establishing a pattern of positive behaviors around AI requires investment and patience. “Behavioral change is crucial to adoption of AI features, but behavioral change always takes longer than you expect. Change management, peer groups, ongoing learning opportunities, and practice are all crucial,” said Jeff Chow, chief product and technology officer at Miro. In genAI, champions programs can embed enthusiastic subject-matter experts into teams, acting as a resource to model and reinforce good AI practices, dramatically expanding the amount of behavioral reinforcement you can provide beyond formal training.
3. Rituals: common, recurring experiences. Creating a cadence of touch points around genAI makes it easier for employees to tap into continuous learning. “We started by setting up recurring lunch-and-learn sessions on Microsoft 365 Copilot, which have been well-attended. We also assign homework to try two or three techniques in advance and then have employees discuss successes and failures,” the CIO of a major oil and gas company told us. Additionally, a weekly email containing organizationally relevant tips, examples, and prompts can become a ritual if employees look forward to reading it every Friday morning — and even more so if managers are taught to ritualize the discussion and reinforcement of those examples.
4. Artifacts: visible resources. Artifacts include those things that employees “see” around them reinforcing the priorities of the culture of the organization. These can include digital artifacts like dynamic, ever-changing prompt libraries or example videos. One Fortune 100 tech firm built content libraries for both technical and nontechnical employees. It created proprietary coursework that built on external content but was tailored to the company’s roles and processes. The company’s SVP of AI told us that tailoring these artifacts specifically to its business plays a key role in ensuring relevance. Digital spaces can act as artifacts, too, like a vigorous Teams or Slack channel that employees can pop into for help writing a prompt or otherwise using AI — especially when the rituals and behaviors manifested in those spaces reinforce the overall shared purpose, bringing the culture together around how AI helps employees and the organization win together.

Supplemental Material Companies We Interviewed For This Report We would like to thank the individuals from the following companies who generously gave their time during the research for this report.

Adobe Adore Me Automation Anywhere Avanade Foundever Genesys Google Grammarly KPMG A large global financial services firm A large auditing and accounting consultancy A large global technology firm A major oil and gas company Microsoft Miro Unisys West Monroe WPP